Transformations and White Point Constraint Solutions for a Novel Chromaticity Space

ABSTRACT

A novel chromaticity space is disclosed that may be used as a framework to implement an auto-white balance solution or other color image processing solutions that take advantage of the particular properties of the novel chromaticity space. The chromaticity space may be defined by using a series of mathematical transformations having parameters that are optimized to adapt to specific sensors&#39; spectral sensitivities. The unique properties of the novel chromaticity space provide a conscious white point constraining strategy with clear physical meaning. In this chromaticity space, the ranges of possible white points under different kinds of lighting conditions can be defined by polygons. Because of the physical meaning the chromaticity space, the projection that is needed to bring an initially “out-of-bounds” white point back into the polygon also carries physical meaning, making the definition of projection behavior and its consequences conceptually clean and predictable.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 61/546,138, filed Oct. 12, 2011, which is hereby incorporatedby reference in its entirety.

BACKGROUND

This disclosure relates generally to the field of image processing. Moreparticularly, but not by way of limitation, it relates to techniques forcreating a novel chromaticity space that may be used as a framework toperform color balancing on images captured by a variety of differentimage sensors.

Color balancing may be thought of as the global adjustment of the colorsin an image. One goal of color balancing is to render specific colors,e.g., neutral white, as accurately as possible to the way the colorappeared in the actual physical scene from which the image was captured.In the case of rendering neutral white colors correctly, the process isoften referred to as “white balancing.” Most digital cameras base theircolor balancing and color correction decisions at least in part on thetype of scene illuminant. For example, the color of a white sheet ofpaper will appear differently under fluorescent lighting than it will indirect sunlight. The type of color correction to be performed may bespecified manually by a user of the digital camera who knows the sceneilluminant for the captured image, or may be set programmatically usingone or more of a variety of automatic white balance (AWB) algorithms.

Chromaticity, as used herein, will refer to an objective specificationof the quality of a color—independent of its luminance. Once luminancehas been removed from consideration, the remaining components of a colorcan be defined by a pair of variables in a two-dimensional space. Thisis useful, as it allows the “chromaticity space” to be mapped into a 2Dgraph where all existing colors may be uniquely identified by an x-ycoordinate position in the chromaticity space. Among the well-knownchromaticity spaces are the a-b space of the CIELAB color space and theu-v space in CIELUV color space.

Generally, the definition of such a color space is notapplication-dependent, although color spaces may be created for aparticular application. For example, the novel chromaticity spacedisclosed herein may be applied, in one embodiment, to scene white pointcalculation. Novel chromaticity spaces, such as those disclosed herein,were designed to work with different imaging sensors having differentspectral sensitivity responses. One property of such novel chromaticityspaces is that various “physical” properties of the chromaticity spacesare able to remain consistent across imaging sensors having variousspectral sensitivity responses. However, the parameters of thecalculations used to transform the image data into such chromaticityspaces may be adaptive from sensor to sensor. Accordingly, the disclosedtechniques provide a series of transformations to define a novelchromaticity space that is conceptually sound and computationallyefficient, e.g., when used to calculate scene white point.

SUMMARY

This disclosure pertains to devices and computer readable media forimplementing a novel chromaticity space and its applications. Thechromaticity space may be used as a working space to implement an autowhite balance (AWB) solution that takes advantage of particularproperties of this chromaticity space. The chromaticity space may bedefined by using a series of mathematical transforms having parametersthat are optimized to adapt to specific sensors' (e.g., CMOS sensors)spectral sensitivities, i.e., the degree to which a particular sensorresponds at any given wavelength of light.

In the final 2D chromaticity space, black body light sourcex-coordinates may form an essentially straight horizontal line. Thehorizontal axis of the chromaticity space is along the blue-reddirection that correlates to correlated color temperature (CCT) of thelight sources. The vertical direction is along the green-purpledirection. Along the vertical direction, if the y-coordinate of anilluminant is farther away from the black body line, the color renderingindex (CRI) of that the light source usually goes lower. The uniqueproperties of the above-defined chromaticity space provide for theapplication of a conscious white point constraining strategy with clearphysical meaning.

In this chromaticity space, the ranges of possible white points underdifferent kinds of lighting conditions can be defined by polygons. Thesepolygons can be degenerated into individual line segments. Because ofthe physical meaning the chromaticity space, the projection that isneeded to bring an initially “out-of-bounds” white point back into thepolygon also carries physical meaning, making the definition ofprojection behavior and its consequences conceptually clean andpredictable. Not only are the projections in this chromaticity spaceconceptually sound, they are also relatively hardware and softwarefriendly, as a projection calculation is only needed for one ordinate.

Thus, in one embodiment described herein, a process that may be carriedout by hardware and/or software is disclosed comprising: obtaining imagedata representing a physical scene from an image captured by an imagesensor of an image capture device; transforming the image data intochrominance values, wherein the chrominance values exist in atwo-dimensional chromaticity space; rotating the chromaticity space toenforce a first chromaticity space physical property; identifying awhite point constraint zone in the chromaticity space, based at least inpart on the first chromaticity space physical property; and thenshearing at least some the transformed image data to enforce a secondchromaticity space physical property.

In some embodiments described herein, the process may then optionallycalculate a scene white point for the image data in the chromaticityspace based on the transformed values and the identified white pointconstraint zone.

A novel and improved chromaticity space with clear physical meaning forconstraining the white point and providing an auto white balanceframework in accordance with the various embodiments described hereinmay be implemented directly by a device's hardware and/or software, thusmaking these robust image processing techniques readily applicable toany number of electronic image capture devices with appropriate imagesensors, such as mobile phones, personal data assistants (PDAs),portable music players, digital cameras, as well as laptop and tabletcomputer systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a perceptual model for correcting camera response tothe sensitivity of the human eye, in accordance with prior artteachings.

FIG. 1B illustrates an abstractive and conceptual image processingpipeline for performing color correction, in accordance with prior artteachings.

FIG. 2 illustrates an improved image processing pipeline for performingcolor balancing and correction utilizing a novel chromaticity space, inaccordance with one embodiment.

FIG. 3 illustrates in greater detail a process for creating a novelchromaticity space, in accordance with one embodiment.

FIG. 4 illustrates a plot of light source white point coordinates in anovel chromaticity space, in accordance with one embodiment.

FIG. 5 illustrates a white point constraint zone in a novel chromaticityspace, in accordance with one embodiment.

FIG. 6 illustrates various white point projection zones in conjunctionwith a white point constraint zone in a novel chromaticity space, inaccordance with one embodiment.

FIG. 7 illustrates a simplified functional block diagram of arepresentative electronic device possessing a display and an imagesensor.

DETAILED DESCRIPTION

The techniques disclosed herein are applicable to any number ofelectronic image capture devices with image sensors, such as digitalcameras, digital video cameras, mobile phones, personal data assistants(PDAs), portable music players, as well as laptop and tablet computersystems.

In the interest of clarity, not all features of an actual implementationare described in this specification. It will of course be appreciatedthat in the development of any such actual implementation (as in anydevelopment project), numerous decisions must be made to achieve thedevelopers' specific goals (e.g., compliance with system- andbusiness-related constraints), and that these goals will vary from oneimplementation to another. It will be further appreciated that suchdevelopment effort might be complex and time-consuming, but wouldnevertheless be a routine undertaking for those of ordinary skill havingthe benefit of this disclosure.

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the inventive concept. As part of the description, somestructures and devices may be shown in block diagram form in order toavoid obscuring the invention. Moreover, the language used in thisdisclosure has been principally selected for readability andinstructional purposes, and may not have been selected to delineate orcircumscribe the inventive subject matter, resort to the claims beingnecessary to determine such inventive subject matter. Reference in thespecification to “one embodiment” or to “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiments is included in at least one embodiment of theinvention, and multiple references to “one embodiment” or “anembodiment” should not be understood as necessarily all referring to thesame embodiment.

Turning first to FIG. 1A, a perceptual model for correcting cameraresponse to the sensitivity of the human eye is shown for explanatorypurposes. At basic level, a camera's image sensor will havecharacteristic responses 100 to incident light across the entirespectrum of wavelengths to which the image sensor is sensitive. Thescene being captured may also be lit by different types of illuminants,which can have an effect on the way that the colors in the scene will bereproduced and perceived by the human eye. Thus, different optimizations102, such as color balancing, may be employed based on differentilluminant types.

If the image sensor's sensitivity is the same as the sensitivity of thehuman eye across the visible ranges of the human eye, then no furthercolor correction beyond color balancing may be needed; however, if theimage sensor's sensitivity and the sensitivity of the human eye aredifferent across the particular range of the human vision, then furthercolor correction, such as the application of a color correction matrix(CCM) may also be employed to the image sensor captured data to ensurethe perception of the color by the human eye 104 is as accurate aspossible to the real-world scene color.

Turning now to FIG. 1B, an abstractive and conceptual image processingpipeline 140 for performing color correction is shown for explanatorypurposes. First, the scene is captured by an image sensor 106. Data isoutput from the image sensor in RGB raw data format 108. Next, a scenewhite point is calculated and a white balance algorithm is run over thecaptured image sensor data 110 to determine if the gains of any of theR, G, or B channels need to be adjusted so that a white pixel renders aswhite. Any of a number of possible white balance algorithms may be used,such a gray world algorithm, selective gray world algorithm, or weightedaccumulator algorithm. Next, depending on the type of illuminant,further color correction 112, for example, a CCM may be applied to theimage data. The values in a CCM may be a function of both the scenewhite point and the scene lux level. Therefore, two images with the samewhite point could have very different CCMs. Once the image data has beencolor balanced and color corrected as desired, output data, e.g., in theform of sRGB (i.e., standard RGB), may be sent to a desired displaydevice 114.

With this framework in mind, the remainder of the Detailed Descriptionsection is divided into the two sections. Section I discusses thedefinition and construction of a novel chromaticity space, and SectionII will discuss the application of the chromaticity space to automaticwhite balance solutions.

Section I—Chromaticity Space

Turning now to FIG. 2, an improved image processing pipeline 150 forperforming color correction utilizing a novel chromaticity space isshown, in accordance with one embodiment. Compared to image processingpipeline 140 shown in FIG. 1B, the improved image processing pipeline150 has an element 109 labeled “Transform RGB raw data into novelchromaticity space” between the receiving of raw data at element 108 andthe performance of white point calculation and color balancing atelement 110′. Element 110′ in FIG. 2 differs from element 110 in FIG. 1Bin that the scene white point is calculated in the novel chromaticityspace constructed in element 109. In this Section, the transformationsof raw data into the novel chromaticity space 109 will be described infurther detail.

According to some embodiments, the novel chromaticity space may bedefined by a series of mathematical transformations in order to give thespace a concise physical meaning. The transformation parameters areoptimized to adapt to specific sensors' spectral sensitivity. Exemplarytransforms and parameters are detailed below.

Linear RGB to RYB Transform

As mentioned above, image sensor information may come into the imageprocessing pipeline 150 from the image sensor in the form of RGB rawdata, i.e., an unprocessed linear RGB signal that is not ready fordisplay. Turning now to FIG. 3, the transformation process flow 109 ofthe novel chromaticity space is shown in greater detail. In order toreduce the input signal from three color dimensions (i.e., red, green,and blue) into two color dimensions, the RGB signal may converted to a“pseudo luminance” signal Y where the Y stands for “pseudo luminance” inthis embodiment (Element 116). Y is called pseudo luminance in thisembodiment because it is not calculated according to the ITU-RRecommendation BT.709 standard definition of luminance (more commonlyknown by the abbreviations Rec. 709 or BT.709) or other standarddefinitions of luminance. Instead, pseudo luminance in this instance maybe calculated as the weighted sum of the input linear RGB signal:

Y=w1*R+w2*G+w2*B, where w1+w2+w3=1.0  (Eqn. 1.1)

The values of the weighting coefficients w1, w2, and w3 may be optimizeda priori for a particular image sensor based on one or more specificphysical constraints imposed upon the novel chromaticity space, as willbe described in further detail later.

Next, the RYB values are converted to chromaticity values, and anoptional nonlinear transform such as log transformation may be appliedaccording to the following equations (Element 118):

c1=log(R)−log(Y)

c2=log(B)−log(Y)  (Eqns. 1.2)

It should be noted that performing a log transformation as shown aboveis but one example of a possible transformation to be applied tochromaticity values, and that other types of transformations, such aspower function, may be applied based on a particular implementation.

Next, values in the 2D chromaticity space whose parameters are derivedfrom Eqns. 1.2 above may be rotated according to a matrix, M, as shownin Eqn. 1.3 (Element 120):

C=M*c, where c is the 2D vector from Eqn. 1.2 consisting of c1 andc2;  (Eqn. 1.3).

The matrix, M, consists of values that will give the chromaticity spaceits unique physical attributes. In one particular embodiment describedherein, the rotation matrix M will rotate the chromaticity space suchthat the “black body” line, which is formed by the chromaticitycoordinates of black body light sources, is essentially parallel withthe horizontal axis of the chromaticity space. The rotation angle ofchromaticity space is then the angle formed by the line connecting the ycoordinates of two chosen blackbody light sources against horizontaldirection. The rotation matrix may be the standard 2D rotation matrixcalculated from the rotation angle, θ:

$M = \begin{bmatrix}{\cos (\theta)} & {- {\sin (\theta)}} \\{\sin (\theta)} & {\cos (\theta)}\end{bmatrix}$

The transformation process may also optionally include the performanceof shear mapping (Element 122) parallel to the x-axis, according to thefollowing matrix multiplication, where k is a shearing coefficient thatis optimized in order to enforce one or more desired chromaticity spacephysical properties:

$\begin{bmatrix}x^{\prime} \\y^{\prime}\end{bmatrix} = {\begin{bmatrix}1 & k \\0 & 1\end{bmatrix}\begin{bmatrix}x \\y\end{bmatrix}}$

Such a calculation may be done, for example, to align all white pointshaving the same correlated color temperature (CCT) along the samex-coordinate in the novel chromaticity space The parameter k is thencalculated accordingly to achieve this goal. The CCT is defined by theInternational Commission on Illumination (CIE) as the temperature of thePlanckian radiator whose perceived color most closely resembles that ofa given stimulus at the same brightness and under specified viewingconditions. In other words, the aim of the CCT is to describe thedominant color of a light source without regard to human visual responseor the type of source illuminant. Lights sources with a correlated colortemperature, i.e., light sources that are not pure incandescentradiators (i.e., not “black bodies”) do not have an equal radiationresponse at all wavelengths in their spectrum. As a result, they canemit disproportionate levels of radiation when rendering certain colors.These light sources are measured in theft ability to accurately renderall colors of their spectrum, in a scale that is called the ColorRendering Index (CRI). CRI is rated on a scale from 1-100. The lower theCRI rating, the less accurately colors will be reproduced by a givenlight source. Light sources that are true incandescent radiators (i.e.,“black bodies”) have a CRI of 100, meaning that all colors in theirspectrum are rendered equally.

At this point, the series of transformations described above will havecreated the final chromaticity space (Element 124) having concisephysical properties and meaning across various image sensors withdiffering spectral sensitivities.

The novel chromaticity space may then optionally be used to accumulatepixel values in the form of a two-dimensional histogram (Element 126).Keeping a 2D histogram of pixel data as represented in the novelchromaticity space provides a rich resource for performing any of anumber of AWB algorithms to generate a scene white point. Further, thedesign of the novel chromaticity spaces described above allows for theapplication of rules with clear physical meaning to analyze the dataaccumulated in the 2D histogram that can provide additionalcomputational efficiency. Of course, use of such an accumulationhistogram is not necessary, and any number of white point calculationmethods or other image processing applications may be employed with thenovel chromaticity space.

As will be discussed in greater detail below, when white balance issubsequently performed (Element 110′) on the data accumulated in thenovel chromaticity space, white point constraint projections may beperformed with greater ease and efficiency, owing to the physicalproperties of the novel chromaticity space.

Properties of the Chromaticity Space

As mentioned above, the parameters of the transformations defined abovethat are used to build the novel chromaticity space may be optimized fora given imaging sensor's spectral sensitivity, such that thechromaticity space will have the following exemplary physical propertiesfor any sensor that is used to capture images. (These chromaticity spaceproperties are described as being “physical” because their enforcementproduces a tangible effect on the shape and arrangement of the resultantchromaticity space that can later be leveraged to make white pointcalculations and projections more efficient.)

1.) In the final 2D chromaticity space, black body light sourcex-coordinates will form an essentially straight horizontal line. Thishorizontal axis is along blue-red direction that corresponds tocorrelated color temperature (CCT). The horizontal axis is said to bealong the “blue-red” direction because cooler light sources, i.e., lightsources with a higher CCT are often described as “blueish,” whereaswarmer light sources, i.e., light sources with a lower CCT are oftendescribed as “reddish.” The straight horizontal line in the final 2Dchromaticity space is achieved through the pseudo luminance calculationusing optimized values for the weighting coefficients w1, w2, and w3described in Eqn. 1.1 above and the rotation matrix, M, described inEqn. 1.3 above. For example, the weighting coefficients may bedetermined for a given sensor by minimizing an error metric, such as thestandard deviation of the transformed black body points from abest-fitting horizontal line in the resultant chromaticity space. Theblack body line formed in the resultant novel chromaticity space isdescribed as being essentially straight because, although it may not beperfectly horizontal in the final chromaticity space, the transformationand rotation parameters will be optimized to make the black body line asstraight as possible. Other techniques may be employed to enforce thisproperty as well, based on a particular implementation and tolerancelevels for enforcement of the property.

2.) The vertical axis in the final 2D chromaticity space is along the“green-purple” direction. This axis is referred to as the “green-purple”direction because, as a light source moves above the black body line,light sources begin to appear more purple, and as light sources movebelow the black body line, the light sources begin to appear greener.Along the vertical axis, as the y-coordinate of a light source movesfarther away from the horizontal blackbody line, the color renderingindex (CRI) of that the light source decreases.

The above two properties establish a Euclidean chromaticity space withsomewhat orthogonal color definitions. The correlation of thex-coordinates in the chromaticity space to the CCT of the light sourceis another important property for color-related manipulation.

Because the weighting coefficients (e.g., w1, w2, and w3) and rotationmatrix (e.g., M) parameters of the chromaticity space are optimized suchthat the unique properties of this chromaticity space described aboveare enforced for any number of particular imaging sensors havingdistinct spectral sensitivity responses, the exact transforms employedwill be different for different sensors, but will result in achromaticity space with the chromaticity space physical propertiesoutlined above. The application of the chromaticity spacetransformations using optimized parameters as outlined above isolatesthe effects of individual sensor spectral response variation that are tobe expected from different sensors made by different manufacturers. Thissensor response transparency allows the transformations to be executedin this chromaticity space independent of sensor type and maintains theability to perform conceptually clean white point calculation andconstraint operations, as will be described below.

FIG. 4 illustrates one embodiment of a 2D plot 128 of light source whitepoint coordinates in the novel chromaticity space discussed above, inaccordance with one embodiment. The horizontal axis of the plot islabeled c1′ (i.e., transformed c1 chromaticity values) and correspondsroughly to CCT. This axis is said to correspond to CCT because thetransformations described above were optimized such that all whitepoints having the same CCT would align along the same x-coordinate inthe novel chromaticity space. Likewise, the vertical axis of the graphis labeled c2′ (i.e., transformed c2 chromaticity values) andcorresponds roughly to CRI. This axis, too, provides a concise physicalmeaning, in that, the horizontal black body line corresponds to a CRIvalue of roughly 100, and thus, the farther a given white point is belowthe black body line, the lower the CRI of that illuminant is. In theplot 128, the circles 130 represent the coordinates of the white pointsof black body radiators of various temperatures. The black bodyradiators form an essentially horizontal line across the domain of CCTs.The diamonds 132 represent daylight with different CCTs. The triangles134 represent other types of light sources, including cool whitefluorescent lights, compact fluorescent lights, LED lights, etc.Triangles falling below the black body line represent more greenishlight sources, such as fluorescent lights.

It should also be noted that the definition of horizontal and verticaldirections are interchangeable. In other words, the rotation of thechromaticity space employed by matrix M in Eqn. 1.3 above could insteadmake the vertical axis correspond to CCT and the horizontal axiscorrespond to CRI.

Section II—White Point Constraining Through Projection

Many variations of AWB and applications employing AWB can be built andrun in this novel chromaticity space. As such, the details of anyparticular AWB algorithm are not discussed in further detail here.Instead, a particular aspect of AWB algorithms, white point constraintthrough projection, will be discussed in the context of the novelchromaticity space that has been characterized and described above.

Due to various limitations and known conditions, an initially calculatedscene white point may be subjected to further constraining so that itfalls within what is considered a valid range for white points. Thisfurther constraint of a calculated white point is often needed in orderto arrive at a final, realistic white point for the scene. For example,if the scene lux is in the sunny daylight range, it makes sense to limitthe white point around the daylight range (see diamonds 132 in FIG. 4).For reference, average indoor lighting ranges from 100 to 1,000 lux, andaverage outdoor sunlight is about 50,000 lux. By measuring scene lux, alikely lighting source type may be inferred, and a range of knownpossible white values for such a light source may be used as anadditional constraint on the white point.

The unique properties of the above-defined chromaticity space allow aconscious white point constraining strategy with clear physical meaningto be employed. FIG. 5 illustrates a white point constraint zone in thenovel chromaticity space, in accordance with one embodiment. In such achromaticity space, the ranges of the white point under different kindsof conditions can be defined by polygons. In one embodiment, a pentagonprovides a convenient definition for the white point boundaries. Thepentagon can be degenerated into a number of line segments that serve toconstrain the white point possible locations. One such exemplary polygonis illustrated by the pentagon shape shown as drawn with thick blacklines FIG. 5.

Looking at FIG. 5, the upper horizontal line is the “black body line.”The two vertical boundaries, labeled “Min CCT” and “Max CCT,” aredefined by the CCT range where white points are allowed to exist. Thebottom projection lines, labeled “CRI Constraint 1” and “CRI Constraint2” complete the total encapsulation of the valid white point zone underthese assumptions. Thus, the pentagon white point zone shown in FIG. 5can be defined by the following exemplary rules as to where the whitepoint can be: RULE 1.) No white point can be above the black body line;RULE 2.) No white point can be outside of a certain CCT range; and RULE3.) No white point can be beyond a certain distance from blackbody whitepoints, depending on its CCT. White points that fall initially outsidethe zone defined by the rules enumerated above can be projected backinto the zone, so that a realistic white point for the scene is chosen(see Element 110′ in FIG. 3).

Because of the physical meaning the chromaticity space, the projectionto bring an initially out-of-bounds white point back into the definedpolygon zone is done according to the physical constraints placed on thedefined white point zone, making the definition of projection behaviorand its consequences conceptually clean and predictable. To illustratethis concept, FIG. 6 shows various white point projection zones inconjunction with a pentagonal white point constraint zone in the novelchromaticity space, in accordance with one embodiment.

For instance, and in reference to FIG. 6, if the initially calculatedwhite point falls outside the defined pentagon and stays in “ProjectionZone 1,” the constraining projection could simply involve projecting they-coordinate of the white point onto the blackbody line (following Rule1, as stated above) and leaving the x-coordinate unchanged, i.e.,performing a constant CCT projection. This projection maintains thewhite point's color temperature, as there is no x-coordinate changeduring the projection process, (Recall that one of the specialproperties of this chromaticity space is that x-coordinates in thechromaticity space correspond to a particular light source CCT.)

As is further illustrated in FIG. 6, white points outside the validwhite point zone in any of the other various Projection Zones may alsobe projected back onto the polygon ring with dean conceptual meaning.For example, calculated white points that fall initially into ProjectionZone 2 or Projection Zone 3 may be projected horizontally onto thepolygon ring, as their CCT is outside the specified allowable range,while their CRI-related coordinate (i.e., c2′) is within the specifiedallowable range. Further, calculated white points that fall initiallyinto Projection Zone 4 or Projection Zone 5 may be projected verticallyup onto the polygon ring, as their CCT is within the specified allowablerange, while their CRI-related coordinate (i.e., c2′) is outside thespecified allowable range. For white points that fall within the zoneslabeled with a combination of projection zones (e.g., “Projection Zone 1& 2” or “Projection Zone 1 & 3”), the projection may be accomplishedseparately and independently via a sequence of single-coordinateprojections, which remains more computationally efficient thanperforming two-coordinate projections. For example, a white pointfalling in “Projection Zone 1 & 2” may first be projected down to theblack body line, keeping the x-coordinate constant, and then projectedover to the MIN CCT line keeping the y-coordinate constant.

A further simplification of the chromaticity space described above maybe employed when the light source is known to be a black body type lightsource. One way to estimate whether a scene is illuminated by a blackbody type light source is to determine whether the scene is illuminatedby a light source with or without flickering. Techniques to determinewhether a scene is illuminated with a flickering light source are knownto those of skill in the art. A flickering light source indicates anartificial light source, and, thus, the black body approximation is nota good one. If no flickering is detected and the light source is deemedto be a black body light source, however, then the projection into thewhite point constraint zone may be simplified to projecting the point upor down onto the blackbody line segment. Again, this type of projectionwould involve only the y-coordinate in this novel chromaticity space.

Thus, not only are the projections in this novel chromaticity spaceconceptually sound, they are also computationally-friendly because, formost applicable cases, only one coordinate of the white point will beinvolved in performing the projection calculation if it is outside thewhite point constraint zone for either axial direction. When the polygonis degenerated into individual line segments, the projectionscalculations are then correspondingly degenerated into point to lineprojections, followed by range-limiting operations if either (or both)of the coordinates of the white point are outside the line segmentrange. For calculated white points falling in a combination ofprojection zones, the range-limiting operations may be handled in thesame way as the polygon projections described above, with the twocoordinates of the white point being handled independently. For acalculated white point that has coordinates inside the line segmentrange, point to line projections may be carried out with either the CCTconstant, the CRI property constant, or using a minimal distance rule.

Referring now to FIG. 7, a simplified functional block diagram of arepresentative electronic device possessing a display 200 according toan illustrative embodiment, e.g., electronic image capture device 200,is shown. The electronic device 200 may include a processor 216, display220, proximity sensor/ambient light sensor 226, microphone 206,audio/video codecs 202, speaker 204, communications circuitry 210,position sensors 224 (e.g., accelerometers or gyrometers), image sensorwith associated camera hardware 208, user interface 218, memory 212,storage device 214, and communications bus 222. Processor 216 may be anysuitable programmable control device and may control the operation ofmany functions, such as the generation and/or processing of imagemetadata, as well as other functions performed by electronic device 200.Processor 216 may drive display 220 and may receive user inputs from theuser interface 218. An embedded processor provides a versatile androbust programmable control device that may be utilized for carrying outthe disclosed techniques.

Storage device 214 may store media (e.g., image and video files),software (e.g., for implementing various functions on device 200),preference information, device profile information, and any othersuitable data. Storage device 214 may include one more storage mediumsfor tangibly recording image data and program instructions, includingfor example, a hard-drive, permanent memory such as ROM, semi-permanentmemory such as RAM, or cache. Program instructions may comprise asoftware implementation encoded in any desired language (e.g., C orC++).

Memory 212 may include one or more different types of memory which maybe used for performing device functions. For example, memory 212 mayinclude cache, ROM, and/or RAM. Communications bus 222 may provide adata transfer path for transferring data to, from, or between at leaststorage device 214, memory 212, and processor 216. User interface 218may allow a user to interact with the electronic device 200. Forexample, the user input device 218 can take a variety of forms, such asa button, keypad, dial, a click wheel, or a touch screen.

In one embodiment, the personal electronic device 200 may be anelectronic device capable of processing and displaying media, such asimage and video files. For example, the personal electronic device 200may be a device such as such a mobile phone, personal data assistant(PDA), portable music player, monitor, television, laptop, desktop, andtablet computer, or other suitable personal device.

The foregoing description of preferred and other embodiments is notintended to limit or restrict the scope or applicability of theinventive concepts conceived of by the Applicant. As one example,although the present disclosure focused on handheld personal electronicimage capture devices, it will be appreciated that the teachings of thepresent disclosure can be applied to other implementations, such astraditional digital cameras, in exchange for disclosing the inventiveconcepts contained herein, the Applicants desire all patent rightsafforded by the appended claims. Therefore, it is intended that theappended claims include all modifications and alterations to the fullextent that they come within the scope of the following claims or theequivalents thereof.

What is claimed is:
 1. A non-transitory program storage device readableby a programmable control device comprising instructions stored thereonto cause the programmable control device to: obtain image data from animage sensor of an image capture device; transform the image data intochrominance values, wherein the chrominance values exist in atwo-dimensional chromaticity space; rotate the chromaticity space toenforce a first chromaticity space physical property; shear at leastsome the transformed image data to enforce a second chromaticity spacephysical property; and identify a white point constraint zone in thechromaticity space, based at least in part on the first chromaticityspace physical property.
 2. The non-transitory program storage device ofclaim 1, further comprising instructions to cause the programmablecontrol device to calculate a white point for the image data in thechromaticity space.
 3. The non-transitory program storage device ofclaim 2, wherein the instructions to cause the programmable controldevice to calculate a white point further comprise instructions to causethe programmable control device to: create a two-dimensional histogramover the chromaticity space; and accumulate the transformed image datain the histogram.
 4. The non-transitory program storage device of claim2, wherein the instructions to cause the programmable control device tocalculate a white point further comprises instructions to cause theprogrammable control device to project the calculated white point backinto the identified white point constraint zone.
 5. The non-transitoryprogram storage device of claim 4, wherein the instructions to cause theprogrammable control device to project the calculated white point backinto the identified white point constraint zone further comprisesinstructions to cause the programmable control device to use asingle-coordinate projection operation.
 6. The non-transitory programstorage device of claim 4, wherein the instructions to cause theprogrammable control device to project the calculated white point backinto the identified white point constraint zone further compriseinstructions that are based at least in part on the first chromaticityspace physical property.
 7. The non-transitory program storage device ofclaim 1, wherein the instructions to cause the programmable controldevice to identify a white point constraint zone in the chromaticityspace are further based at least in part on the second chromaticityspace physical property.
 8. The non-transitory program storage device ofclaim 1, wherein the first chromaticity space physical propertycomprises black body light source coordinates forming an essentiallystraight line over a first range when plotted in the chromaticity space.9. The non-transitory program storage device of claim 8, wherein thesecond chromaticity space physical property comprises a color renderingindex (CRI) of a light source decreasing the farther it moves away fromthe essentially straight line.
 10. The non-transitory program storagedevice of claim 1, wherein the white point constraint zone comprises apolygonal ring.
 11. The non-transitory program storage device of claim9, wherein the white point constraint zone comprises a polygonal ring.12. The non-transitory program storage device of claim 1, wherein theinstructions to cause the programmable control device to transform theimage data into chrominance values comprise instructions to cause theprogrammable control device to use weighting coefficients optimized forthe spectral sensitivity of the image sensor.
 13. The non-transitoryprogram storage device of claim 1, wherein the instructions to cause theprogrammable control device to enforce the first chromaticity spacephysical property comprise instruction that are based at least in parton the spectral sensitivity of the image sensor of the image capturedevice.
 14. The non-transitory program storage device of claim 1,wherein the first and second chromaticity space physical properties arethe same.
 15. An apparatus comprising: an image sensor; a programmablecontrol device; a memory coupled to the image sensor and theprogrammable control device, wherein instructions are stored in thememory, the instructions, when executed by the programmable controldevice, cause the programmable control device to: obtain RGB image datafrom an image captured by the image sensor; calculate a weighted sum ofthe RGB image data using a plurality of weights to generate a pseudoluminance value (Y), wherein the plurality of weights are optimizedbased on a spectral sensitivity of the image sensor, and wherein thegenerated pseudo luminance value (Y) is used to generate RYB image data;perform a non-linear transform on the RYB image data; transform thenon-linear RYB image data into chrominance values, wherein thechrominance values exist in a two-dimensional chromaticity space; rotatethe chromaticity space to enforce a first chromaticity space physicalproperty; and identify a white point constraint zone in the chromaticityspace based, at least in part, on the first chromaticity space physicalproperty.
 16. The apparatus of claim 15, wherein the instructions storedin the memory further comprise instructions to cause the programmablecontrol device to scale at least some of the transformed non-linear RYBimage data to fit in the chromaticity space.
 17. The apparatus of claim15, wherein the instructions stored in the memory further compriseinstructions to cause the programmable control device to perform a shearoperation on the transformed non-linear RYB image data to enforce asecond chromaticity space physical property.
 18. The apparatus of claim15, wherein the instructions stored in the memory further compriseinstructions to cause the programmable control device to calculate awhite point for the image in the chromaticity space.
 19. The apparatusof claim 18, wherein the instructions to cause the programmable controldevice to calculate a white point further cause the programmable controldevice to project the calculated white point back into the identifiedwhite point constraint zone.
 20. The apparatus of claim 19, wherein theinstructions to cause the programmable control device to project thecalculated white point back into the identified white point constraintzone are based at least in part on the first chromaticity space physicalproperty.
 21. The apparatus of claim 15, wherein the white pointconstraint zone comprises a polygonal ring.
 22. The apparatus of claim15, wherein the instructions to cause the programmable control device toenforce the first chromaticity space physical property are based atleast in part on the spectral sensitivity of the image sensor.
 23. Anembedded logic device that is configured to: obtain image data from animage captured by an image sensor; transform the image data intochrominance values, wherein the chrominance values exist in atwo-dimensional chromaticity space; rotate the chromaticity space tocause black body light source coordinates to form an essentiallystraight line in the chromaticity space; and scale at least some of thetransformed image data to fit in the chromaticity space.
 24. Theembedded logic device of claim 23, wherein the act of transforming theimage data into chrominance values further comprises calculating apseudo luminance value using a plurality of determined weightingcoefficients.
 25. A non-transitory program storage device readable by aprogrammable control device comprising instructions stored thereon tocause the programmable control device to: obtain image data from animage captured by an image sensor of a device; transform the image datainto chrominance values, wherein the chrominance values exist in atwo-dimensional chromaticity space; rotate the chromaticity space tocause black body light source coordinates to form an essentiallystraight line in the chromaticity space; identify a white pointconstraint zone in the chromaticity space, based at least in part on thelocation of the essentially straight line in the chromaticity space; andcalculate a white point for the image data in the chromaticity spacebased, at least in part, on the identified white point constraint zone.